Custom models
In Amazon SageMaker Canvas, you can train custom machine learning models tailored to your specific data and use case. By training a custom model on your data, you are able to capture characteristics and trends that are specific and most representative of your data. For example, you might want to create a custom time series forecasting model that you train on inventory data from your warehouse to manage your logistics operations.
Canvas supports training a range of model types. After training a custom model, you can evaluate the model's performance and accuracy. Once satisfied with a model, you can make predictions on new data, and you also have the option to share the custom model with data scientists for further analysis or to deploy it to a SageMaker hosted endpoint for real-time inference, all from within the Canvas application.
You can train a Canvas custom model on the following types of datasets:
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Tabular (including numeric, categorical, timeseries, and text data)
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Image
The following table shows the types of custom models that you can build in Canvas, along with their supported data types and data sources.
| Model type | Example use case | Supported data types | Supported data sources |
|---|---|---|---|
Numeric prediction |
Predicting house prices based on features like square footage |
Numeric |
Local upload, Amazon S3, SaaS connectors |
2 category prediction |
Predicting whether or not a customer is likely to churn |
Binary or categorical |
Local upload, Amazon S3, SaaS connectors |
3+ category prediction |
Predicting patient outcomes after being discharged from the hospital |
Categorical |
Local upload, Amazon S3, SaaS connectors |
Time series forecasting |
Predicting your inventory for the next quarter |
Timeseries |
Local upload, Amazon S3, SaaS connectors |
Single-label image prediction |
Predicting types of manufacturing defects in images |
Image (JPG, PNG) |
Local upload, Amazon S3 |
Multi-category text prediction |
Predicting categories of products, such as clothing, electronics, or household goods, based on product descriptions |
Source column: text Target column: binary or categorical |
Local upload, Amazon S3 |
Get started
To get started with building and generating predictions from a custom model, do the following:
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Determine your use case and type of model that you want to build. For more information about the custom model types, see How custom models work. For more information about the data types and sources supported for custom models, see Data import.
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Import your data into Canvas. You can build a custom model with any tabular or image dataset that meets the input requirements. For more information about the input requirements, see Create a dataset.
To learn more about sample datasets provided by SageMaker with which you can experiment, see Sample datasets in Canvas.
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Build your custom model. You can do a Quick build to get your model and start making predictions more quickly, or you can do a Standard build for greater accuracy.
For numeric, categorical, and time series forecasting model types, you can clean and prepare your data with the Data Wrangler feature. In Data Wrangler, you can create a data flow and use various data preparation techniques, such as applying advanced transforms or joining datasets. For image prediction models, you can Edit an image dataset to update your labels or add and delete images. Note that you can't use these features for multi-category text prediction models.
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Evaluate your model's performance and determine how well it might perform on real-world data.
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(Optional) For certain model types, you can collaborate with data scientists in Amazon SageMaker Studio Classic who can help review and improve your model.
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Make single or batch predictions with your model.
Note
If you already have a trained model in Amazon SageMaker Studio Classic that you’d like to share with Canvas, you can bring your own model to SageMaker Canvas. Review the BYOM prerequisites to determine whether your model is eligible for sharing.
